基于Transformer的裸盖菇素转录响应Delta表达编码器：架构、表征与生物学验证
A Transformer-Based Delta Expression Encoder for Psilocybin Transcriptional Response: Architecture, Representations, and Biological Validation
浏览论文内容
中文总结 AI 辅助
提出基于Transformer的delta表达编码器，无需先验知识即可从单核RNA测序数据分类裸盖菇素转录响应，实现69.4%准确率，并发现下调更刻板、HTR2A表达与反应可分离性负相关等新见解。
中文摘要 AI 辅助
理解个体对裸盖菇素反应差异，需要在细胞类型水平上建模该药物的转录扰动特征。我提出了一种基于Transformer的delta表达编码器，该编码器无需通路注释或先验生物学知识的监督，即可从单核RNA测序数据中学习分类差异基因表达状态——上调、下调或中性。模型在来自Liao等人2025年数据集的623个样本的伪bulk谱上进行训练，这些样本涵盖18种细胞类型、2种药物条件和6个时间点，并实现了69.4%的加权分类准确率。本文报告了三个主要发现，以及对一个已发表假设的直接检验，该检验结果与该假设不一致。首先，每种细胞类型的分类准确率范围从28.3%（L2/3 IT，一种主要的表达HTR2A的裸盖菇素靶标）到99.6%（内皮细胞），这与已知的裸盖菇素反应生物学一致。其次，裸盖菇素诱导的转录下调在不同个体间比上调更具刻板性（Mann-Whitney U=18615.0，p<0.0001），这是一个新发现，且在兴奋性亚型中呈现皮层深度梯度。第三，注意力引导的基因共调控分析无需通路监督即可恢复药物特异性模块。另外，对基线HTR2A表达是否预测跨细胞类型药物反应可分离性的直接检验发现显著负相关（Spearman r = -0.7088，p = 0.0021），这与简单的HTR2A门控解释所预测的相反。
英文摘要
Understanding why individuals respond differently to psilocybin requires modeling the drug's transcriptional perturbation signature at the cell-type level. I present a Transformer-based delta expression encoder that learns to classify differential gene expression status - upregulated, downregulated, or neutral - from single-nucleus RNA-sequencing data, without supervision from pathway annotations or prior biological knowledge. The model is trained on pseudobulk profiles from 623 examples spanning 18 cell types, 2 drug conditions, and 6 timepoints derived from the Liao et al. 2025 dataset, and achieves 69.4% weighted classification accuracy. Three principal findings are reported, alongside one direct test of a published hypothesis that returned a result inconsistent with that hypothesis. First, per-cell-type classification accuracy ranges from 28.3% (L2/3 IT, a primary HTR2A-expressing psilocybin target) to 99.6% (endothelial cells), consistent with known psilocybin response biology. Second, psilocybin-induced transcriptional downregulation is significantly more stereotyped across individuals than upregulation (Mann-Whitney U=18615.0, p<0.0001), a novel finding with a cortical depth gradient across excitatory subtypes. Third, attention-guided gene co-regulation analysis recovers drug-specific modules without pathway supervision. Separately, a direct test of whether baseline HTR2A expression predicts drug-response separability across cell types found a significant negative correlation (Spearman r = -0.7088, p = 0.0021), the opposite of what a simple HTR2A-gating account would predict.
发表机构
- Stanford School of Medicine（斯坦福大学医学院）
机构由 AI 辅助整理，请以论文原文为准。
